• Vanessa Gómez-Verdejo, Manel Martínez-Ramón, José Florensa-Vila, and Antonio Oliviero.
• Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, Leganés, Madrid, Spain. vanessa@tsc.uc3m.es
• Med Image Anal. 2012 Feb 1; 16 (2): 451-8.

AbstractNeuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). Here, a mutual information (MI) criterion is used to identify regionally specific effects produced by a task. In particular, two MI estimators are presented for its use in fMRI data. The first one uses a Parzen probability density estimation, and the second one is based on a K Nearest Neighbours (KNN) estimation. Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.Copyright © 2011 Elsevier B.V. All rights reserved.

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